{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "a838e80b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x1ad381e02b0>]"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn import linear_model\n",
    "import matplotlib.pyplot as plt#用于作图\n",
    "import numpy as np#用于创建向量\n",
    "reg=linear_model.LinearRegression()\n",
    "x=[[1],[4],[5],[7],[8]]\n",
    "y=[1.002,4.1,4.96,6.78,8.2]\n",
    "reg.fit(x,y)\n",
    "k=reg.coef_#获取斜率w1,w2,w3,...,wn\n",
    "b=reg.intercept_#获取截距w0\n",
    "x0=np.arange(0,10,0.2)\n",
    "y0=k*x0+b\n",
    "plt.scatter(x,y)\n",
    "plt.plot(x0,y0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "1df144c2",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0.97588271 0.03692946] -0.011345504840941878\n"
     ]
    }
   ],
   "source": [
    "from sklearn import linear_model\n",
    "reg=linear_model.LinearRegression()\n",
    "x=[[1,3],[4,2],[5,1],[7,4],[8,9]]\n",
    "y=[1.002,4.1,4.96,6.78,8.2]\n",
    "reg.fit(x,y)\n",
    "k=reg.coef_#获取斜率w1,w2,w3,...,wn\n",
    "b=reg.intercept_#获取截距w0\n",
    "print(k,b)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "1ef3e37e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "88\n"
     ]
    }
   ],
   "source": [
    "print(\"88\")"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "ecoBDP",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.19"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}
